A personalized semi-automatic sleep spindle detection (PSASD) framework.

Background: Sleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone.
New Methods: A Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components.
Results: A single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures.
Comparison With Existing Methods: PSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches.
Conclusions: Overall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.
Competing Interests: Declaration of Competing Interest The authors do not have any conflicts of interest to disclose. This work was supported by the National Institute on Aging (BJAP; R01AG057901), the McDonnell Center for Systems Neuroscience (MK, BJAP), the National Institute of Mental Health K01MH128663 (MK), and the National Institute of Neurological Diseases and Stroke (Y-ESJ; K23NS089922).
(Copyright © 2024. Published by Elsevier B.V.)